Offset Free Tracking Predictive Control Based on Dynamic PLS Framework

Abstract

This paper develops an offset free tracking model predictive control based on a dynamic partial least square (PLS) framework. First, state space model is used as the inner model of PLS to describe the dynamic system, where subspace identification method is used to identify the inner model. Based on the obtained model, multiple independent model predictive control (MPC) controllers are designed. Due to the decoupling character of PLS, these controllers are running separately, which is suitable for distributed control framework. In addition, the increment of inner model output is considered in the cost function of MPC, which involves integral action in the controller. Hence, the offset free tracking performance is guaranteed. The results of an industry background simulation demonstrate the effectiveness of proposed method.

Linear Offset-Free Model Predictive Control in the Dynamic PLS Framework

“…The characteristic of covariance of it is shown as Proposition 2. Based on Equation (19), the optimal future increment of score vector ∆t r can be obtained, as…”

“…Tianyi Gao et al [18] proposed a new intelligent MPC strategy in modified PLS framework, where iterative regression in model building and the large number of important undetermined parameters are avoided. Jin et al [19] proposed an offset-free MPC in PLS framework which involves integral action in the controller and guaranteed offset-free tracking performance.…”